SoroushVahidi/scidocs huggingface dataset:
MOS PARAMETER EXTRACTION AND OPTIMIZATION WITH GENETIC ALGORITHM
(2009)

Extracting an optimal set of parameter values for a MOS device is great importance in contemporary technology is a complex problem. Traditional methods of parameter extraction can produce far from optimal solutions because of the presence of local optimum in the solution space. Genetic algorithms are well suited for finding near optimal solutions in irregular parameter spaces. In this study*, We have applied a genetic algorithm to the problem of device model parameter extraction and are able to produce models of superior accuracy in much less time and with less reliance on human expertise. MOS transistor’s parameters have been extracted and optimized with genetic algorithm. 0.35μm fabricated by C35 process have been used for the results of experimental studies of parameter extraction. Extracted parameters’ characteristic data results have been compared with measurement results. Different values of parameters of genetic algorithm, such as population size, crossover rate , and generation size are compared by different tests.
A study of smoothing methods for language models applied to information retrieval
(2004)

Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing, the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections. Experimental results show that not only is the retrieval performance generally sensitive to the smoothing parameters, but also the sensitivity pattern is affected by the query type, with performance being more sensitive to smoothing for verbose queries than for keyword queries. Verbose queries also generally require more aggressive smoothing to achieve optimal performance. This suggests that smoothing plays two different role---to make the estimated document language model more accurate and to "explain" the noninformative words in the query. In order to decouple these two distinct roles of smoothing, we propose a two-stage smoothing strategy, which yields better sensitivity patterns and facilitates the setting of smoothing parameters automatically. We further propose methods for estimating the smoothing parameters automatically. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to---or better than---the best results achieved using a single smoothing method and exhaustive parameter search on the test data.
RCS reduction of Antipodal Vivaldi Antenna
(2015)

A novel Antipodal Vivaldi Antenna with low radar cross section (RCS) is proposed in this paper. By using flat corrugated slotline to replace exponential gradient curve on both sides of the antenna, the RCS in the endfire direction can be reduced in the operating band of 4.3 GHz-12 GHz when the incident wave is perpendicular to the antenna plane. Mean while, the lowest operating frequency of the antenna is reduced from 4.3 GHz to 3.8 GHz, and radiation performance keeps stably.
Can soft biometric traits assist user recognition
(2004)

Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as “soft” biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves ( ≈ 6%) the recognition performance of the fingerprint biometric system.
Path Integral Networks: End-to-End Differentiable Optimal Control
(2017)

In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan. PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning. Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.
From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems
(2016)

Way beyond its industrial roots, robotics evolved to be a highly interdisciplinary field with a variety of applications in a smart world. The eRobotics methodology addresses this evolution by providing platforms where roboticist can exchange ideas and collaborate with experts from other disciplines for developing complex technical systems and automated solutions. Virtual Testbeds are the central method in eRobotics, where complex technical systems and their interaction with prospective working environments are first designed, programmed, controlled and optimized in simulation before commissioning the real system. On the other hand, Industry 4.0 concepts promote the notion of “Digital Twins”, virtual substitutes of real world objects consisting of virtual representations and communication capabilities making up smart objects acting as intelligent nodes inside the internet of things and services. Combining these two approaches, Virtual Testbeds and Digital Twins, leads to a new kind of “Experimentable Digital Twins” breaking new ground in the simulation-based development and operation of complex technical systems. In this contribution, we describe how such “Experimentable Digital Twins” can act as the very core of simulation-based development processes streamlining the development process, enabling detailed simulations at system level and realizing intelligent systems. Besides this, the multiple use of models and simulations in various scenarios significantly reduces the effort for the use of simulation technology throughout the life cycle of complex technical systems.
Design of a new broadband EMC double ridged guide horn antenna
(2006)

A new design for broadband EMC double ridged guide horn (DRGH) antennas is presented. A conventional 1-18 GHz double ridged guide horn has been investigated rigorously. Then some modifications have been performed in the structure of the antenna. Elimination of radiation pattern deficiencies especially at higher frequencies accompanying with better EM characteristics of the antenna have been the main purposes for these modifications. The main modifications are imposed on the profile of ridges, H-plane flares and E-plane flares. The resulting antenna not only has considerably better performance but also has smaller physical dimensions and less weight in comparison with the conventional one.
Sensitivity analysis of a 1 to 18 GHz broadband DRGH antenna
(2006)

In this paper some properties of a 1-18 GHz double ridged guide horn antenna (DRGH) with a feeding section including coaxial input and a back shorting plate are rigorously investigated. Most desired electromagnetic characteristics of this antenna is achieved by empirically finding sizes for different parameters, however there is no explanation for the effect of most of them in open literature. In order to have a clear idea of the effects of different parameters, a 1-18 GHz DRGH has been simulated with HFSS. It is understood from the results that the parameters near feeding point such as the initial distance between ridges, the distance between the center of the probe and the cavity, and the radius of the inserted probe play a significant role in controlling VSWR and gain and in shaping the radiation pattern for high frequencies
Partially Dielectric-Loaded Ridged Horn Antenna Design for Ultrawideband Gain and Radiation Performance Enhancement
(2012)

In this letter, a partially dielectric-loaded double-ridged horn antenna (PD-DRHA) that operates at 1-18-GHz frequency band is proposed for ultrawideband applications. The partially dielectric loading technique using a small lens inward the aperture has been implemented to enhance the gain of the standard double-ridged horn antenna (DRHA), without changing the physical dimensions. Approximately 5 dB gain increment level is achieved from 5 to 15 GHz with slight changes on return loss characteristics. Moreover, the pattern distortion of DRHA typically between 12-18-GHz band is improved. The antenna gain, radiation pattern, and input reflection performance measurements are presented with comparisons.
A NOVEL DUAL-POLARIZED DOUBLE-RIDGED HORN ANTENNA FOR WIDEBAND APPLICATIONS
(2007)

Dual-polarized antenna is widely used in communication systems such as ECM and DF systems. In this paper a novel doubleridged horn antenna with dual polarizations is introduced for frequency range of 8–18 GHz. Common double ridged horn antennas have single polarization over the operating frequency. We have used five layers polarizer to provide dual polarizations performance of the doubleridged horn antenna. In order to achieve dual polarizations the strips width, strips spacing and layers distances are optimized. It is worth mentioning that the corresponding VSWR of the antenna during the optimization process should be maintain below a certain value (VSWR< 2). Simulation results show that the proposed antenna yields dual polarizations performance and low VSWR over the operating frequency. We have used CST software for antenna simulation which is based on the finite integral technique.
An Improved Design for a 1–18 GHz Double-Ridged Guide Horn Antenna
(2012)

It is a well known fact that the traditional 1-18 GHz double ridge guide horn (DRGH) antenna suffers from pattern deterioration above 12 GHz. At these frequencies, instead of maintaining a single main lobe radiation pattern, the pattern splits up into four lobes. It was shown in the literature that higher order modes are causing the pattern breakup. A benchmark study is performed to establish the performance of typical current and historic 1-18 GHz DRGH antennas. The performance of the antennas are evaluated in terms of gain, VSWR and radiation patterns. An improved 1-18 GHz DRGH antenna is presented. The new design has better gain and VSWR performance without any pattern deterioration. It also consists of significantly fewer parts, reducing the possibility of performance deterioration due to gaps between parts. Two prototypes of the new design were manufactured and tested with excellent agreement between measured and simulated results. The aperture dimensions of the new design are identical to that of the traditional DRGH, making it the only 1-18 GHz DRGH without pattern breakup whose aperture dimensions comply with the requirements specified in MIL-STD-461F - 24.2 by 13.6 cm.
AFFDEX SDK: A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit
(2016)

We present a real-time facial expression recognition toolkit that can automatically code the expressions of multiple people simultaneously. The toolkit is available across major mobile and desktop platforms (Android, iOS, Windows). The system is trained on the world's largest dataset of facial expressions and has been optimized to operate on mobile devices and with very few false detections. The toolkit offers the potential for the design of novel interfaces that respond to users' emotional states based on their facial expressions. We present a demonstration application that provides real-time visualization of the expressions captured by the camera.
Nonparametric variable importance using an augmented neural network with multi-task learning
(2018)

In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome. It is useful to consider this variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data. In this paper, we connect these ideas in nonparametric variable importance to machine learning, and provide a method for efficient estimation of variable importance when building a predictive model using a neural network. We show how a single augmented neural network with multi-task learning simultaneously estimates the importance of many feature subsets, improving on previous procedures for estimating importance. We demonstrate on simulated data that our method is both accurate and computationally efficient, and apply our method to both a study of heart disease and for predicting mortality in ICU patients.
Temporal Data Mining: An Overview
(2011)

— To classify data mining problems and algorithms we used two dimensions: data type and type of mining operations. One of the main issue that arise during the data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.
A practical solution for scripting language compilers
(2009)

Although scripting languages are becoming increasingly popular, even mature scripting language implementations remain interpreted. Several compilers and reimplementations have been attempted, generally focusing on performance.
 Based on our survey of these reimplementations, we determine that there are three important features of scripting languages that are difficult to compile or reimplement. Since scripting languages are defined primarily through the semantics of their original implementations, they often change semantics between releases. They provide large standard libraries, which are difficult to re-use, and costly to reimplement. They provide C APIs, used both for foreign-function-interfaces and to write third-party extensions. These APIs typically have tight integration with the original implementation. Finally, they support run-time code generation. These features make the important goal of correctness difficult to achieve.
 We present a technique to support these features in an ahead-of-time compiler for PHP. Our technique uses the original PHP implementation through the provided C API, both in our compiler, and an our generated code. We support all of these important scripting language features, particularly focusing on the correctness of compiled programs. Additionally, our approach allows us to automatically support limited future language changes. We present a discussion and performance evaluation of this technique, which has not previously been published.
Clinical narrative classification using discriminant word embeddings with ELM
(2016)

Clinical texts are inherently complex due to the medical domain expertise required for content comprehension. In addition, the unstructured nature of these narratives poses a challenge for automatically extracting information. In natural language processing, the use of word embeddings are an effective approach to generate word representations (vectors) in a low dimensional space. In this paper we use a log-linear model (a type of neural language model) and Linear Discriminant Analysis with a kernel-based Extreme Learning Machine (ELM) to map the clinical texts to the medical code. Experimental results on clinical texts indicate improvement with ELM in comparison to SVM and neural network approaches.
The Pricing of Investment Grade Credit Risk during the Financial Crisis
(2009)

This paper uses a structural model to investigate the pricing of investment grade credit risk during the nancial crisis. Our analysis suggests that the dramatic recent widening of credit spreads is highly consistent with the decline in the equity market, the increase in its long-term volatility, and an improved investor appreciation of the risks embedded in structured products. In contrast to the main argument in favor of using government funds to help purchase structured credit securities, we nd little evidence that suggests these markets are experiencing re sales. Preliminary and Incomplete. Coval: Harvard Business School; jcoval@hbs.edu. Jurek: Bendheim Center for Finance, Princeton University; jjurek@princeton.edu. Sta¤ord: Harvard Business School; esta¤ord@hbs.edu. We thank Stephen Blythe, Ken Froot, Bob Merton, André Perold, and David Scharfstein for valuable comments and discussions.
A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors
(2014)

High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.
Differentiable Parameterization of Catmull-Clark Subdivision Surfaces
(2004)

Subdivision-based representations are recognized as important tools for the generation of high-quality surfaces for Computer Graphics. In this paper we describe two parameterizations of Catmull-Clark subdivision surfaces that allow a variety of algorithms designed for other types of parametric surfaces (i.e., B-splines) to be directly applied to subdivision surfaces. In contrast with the natural parameterization of subdivision surfaces characterized by diverging first order derivatives around extraordinary vertices of valence higher than four, the derivatives associated with our proposed methods are defined everywhere on the surface. This is especially important for Computer-Aided Design (CAD) applications that seek to address the limitations of NURBS-based representations through the more flexible subdivision framework.
Identifying differentially expressed genes using false discovery rate controlling procedures
(2003)

MOTIVATION
DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the naïve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation.


RESULTS
Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the naïve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control.


AVAILABILITY
An R program that adjusts p-values using FDR controlling procedures is freely available over the Internet at www.math.tau.ac.il/~ybenja.
Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks
(2017)

With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation may be challenging. In this paper, we study the problem of semi-dense depth map interpolation along with low resolution depth map upsampling. We present an end-to-end learnable residual convolutional neural network architecture that achieves fast interpolation of semi-dense depth maps with different sparse depth distributions: uniform, sparse grid and along intensity image gradient. We also propose a loss function combining classical mean squared error with perceptual loss widely used in intensity image super-resolution and style transfer tasks. We show that with some modifications, this architecture can be used for depth map super-resolution. Finally, we evaluate our results on both synthetic and real data, and consider applications for autonomous vehicles and creating AR/MR video games.